Deeplearning4j version: 0.8.0; OS: Ubuntu 16.04.1 LTS; Java version: 1.8.0_121

GradientTest.java

public class GradientTest {

    private static final Logger logger = LoggerFactory.getLogger(GradientTest.class);

    public static void main(String[] args) {

        //Create the model
      int nIn = 2;
      int nOut = 1;
      Nd4j.getRandom().setSeed(12345);
      MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
            .seed(12345)
            .activation(Activation.RELU)
            .weightInit(WeightInit.XAVIER)
            .updater(Updater.SGD)
            .learningRate(0.1)
            .list()
            .layer(0, new DenseLayer.Builder().nIn(nIn).nOut(3).build())
            .layer(1, new DenseLayer.Builder().nIn(3).nOut(3).build())
            .layer(2, new DenseLayer.Builder().activation(Activation.TANH).nIn(3).nOut(nOut).build())
            .backprop(true).pretrain(false)
            .build();

      MultiLayerNetwork model = new MultiLayerNetwork(conf);
      model.init();

      logger.info("Number parameters: " + model.numParams() + ", Number layers: " + model.getnLayers());
      logger.info("Number params layer 1: " + model.getLayer(0).numParams() + ", params: " + model.getLayer(0).params().toString());
      logger.info("Number params layer 2: " + model.getLayer(1).numParams() + ", params: " + model.getLayer(1).params().toString());
      logger.info("Number params layer 3: " + model.getLayer(2).numParams() + ", params: " + model.getLayer(2).params().toString());

      //Calculate gradient with respect to an external error
      int minibatch = 1;
      INDArray input = Nd4j.rand(minibatch, nIn);
      INDArray output = model.output(input);          //Do forward pass. Normally: calculate the error based on this

      logger.info("Input: " + input.toString());
      logger.info("Output: " + output.toString());

      List<INDArray> zValues = model.computeZ(input.getRow(0), false);

      logger.info("wrong z values: " + zValues.toString());
      logger.info("pre-output: " + model.preOutput(input.getRow(0)));


      zValues = new ArrayList<>();
      zValues.add(input);
      for (int i = 0; i < model.getnLayers(); i++)
      {
         zValues.add(model.zFromPrevLayer(i, input, false));
         input = model.activationFromPrevLayer(i, input, false);
      }

      logger.info("correct z values: " + zValues.toString());

    }

}